Add Ave Record

When to use this skill

Use Add Ave Record when an AI agent needs a reusable SKILL.md workflow for this job: The main workflow for this repo. Adds one new AVE record end to end.

When not to use it

Skip Add Ave Record when the task is outside the coding category, or when a more specific skill in this directory already covers the same workflow with clearer triggers.

How to install

1. Personal install: create ~/.claude/skills/add-ave-record/SKILL.md (and any bundled scripts) so Claude Code, Claude Desktop, and compatible agents can load it in every project.
2. Project install: commit the same folder at .claude/skills/add-ave-record/ so teammates get the skill with the repo.
3. Restart the agent session after copying files so it re-scans the skills directory, then ask for the task in words that match the skill description.

Full install guide for Claude, Cursor, and Codex

What this skill does

# add-ave-record

The main workflow for this repo. Adds one new AVE record end to end.

## Steps

### 1. Grill the vulnerability class first Run grill-with-docs. Answer: - What does a vulnerable component DO? (behavioral_fingerprint) - What attack_class is this? (new or existing?) - What is the worst-case impact? (drives AIVSS) - Which engines can detect it? (evidence_basis_engines) - Can a static scan see it, or does it need runtime? (detection_stage) - Does it chain with other AVEs? (derivable_into)

### 2. Assign the next ave_id Format AVE-YYYY-NNNNN. Never reuse a number. Check records/ for the highest.

### 3. Write the record JSON records/AVE-YYYY-NNNNN.json. Must validate against the schema. Include the evidence fields: - evidence_kind_default - detection_stage - detection_layer - confidence_baseline - evidence_basis_engines - derivable_into

### 4. Write conformance fixtures (TDD — fixtures first) tests/fixtures/AVE-YYYY-NNNNN_positive.md — a conforming implementation MUST flag this tests/fixtures/AVE-YYYY-NNNNN_negative.md — a conforming implementation MUST NOT flag this The negative fixture is the false-positive guard. Make it realistic — a benign file that looks similar to the malicious one.

### 5. Open a coordinated detection-rule PR Detection rule implementations (pattern, YARA, semgrep, or anything else) are implementation artifacts, not standard artifacts — they live in whichever tool implements against this standard, not in this repo. Open a PR in that tool's own repo (e.g. bawbel/scanner) referencing the ave_id and the fixtures above; see CONTRIBUTING.md Step 4.

### 6. Validate ```bash python scripts/validate_records.py pytest tests/ -x -q ```

If validate_records.py fails on AIVSS arithmetic, fix the record's own aarf/cvss_base/thm/mitigation_factor values or the stated aivss_score, don't just adjust one to match the other without checking which one is actually wrong; a computed mismatch usually means the record was drafted against a different set of factors than what got written down.

### 7. Publish A record passing validation is not yet a published one. Update:

- **dist/ave-records-latest.json**: add or replace this record's entry, keeping the array sorted by ave_id. - **CHANGELOG.md**: one line under Unreleased/Added: the ave_id, title, severity, and aivss_score. - **README.md, three separate things, don't assume any of them share a format**:

1. **Prose record count.** Find it first: ```bash grep -n "[0-9]\+ records\|[0-9]\+ behavioral class" README.md ``` Update to the real count from `ls records/AVE-*.json | wc -l`, not by incrementing the old number, more than one record can land in a single batch.

2. **A record-count badge**, if one exists. Badges are usually shields.io-style, with the count embedded as a URL path segment, not free prose, so the prose grep above won't reliably catch it. Find it separately: ```bash grep -n "shields.io\|badge.*record\|records.*badge" README.md ``` If found, the count sits inside the badge URL itself (something like `.../badge/records-59-blue`), update that specific segment to the real count, don't touch the rest of the badge's color, label text, or link target.

3. **A list or table enumerating individual records**, if one exists. This is not a number to update, it needs a new row appended for whatever record just landed, matching the exact column structure and formatting of the existing rows exactly, so it doesn't stand out as the one inconsistently-formatted entry. Find it first: ```bash grep -n "AVE-2026-" README.md ``` If this is a comprehensive, actively-maintained list, append the new record's row after whatever the file's own existing ordering convention is (chronological, by ID, by severity, confirm which before assuming). If it's a curated set of examples rather than a complete enumeration (a handful of illustrative records, not all of them), don't add to it automatically, that's an editorial decision about which records are worth featuring, not a mechanical update; ask before changing this one.

For all three: if the grep for any of them finds nothing, that specific piece doesn't exist in README.md, skip it, don't invent one. If any grep finds something whose format doesn't match what's described above, stop and ask rather than force an edit that might not fit.

Do not bump schema_version or create a new versioned dist snapshot (dist/ave-records-vX.Y.Z.json) as part of this step. That's a separate, deliberate decision tied to an actual schema change, not something that happens automatically because one record got added.

## Severity / AIVSS consistency

CRITICAL → aivss_score >= 9.0 HIGH → 7.0 to 8.9 MEDIUM → 4.0 to 6.9 LOW → < 4.0

If severity and aivss_score disagree, the record fails validation.

A mechanism that reads as severe in plain English can still land MEDIUM, correctly, if it's narrow and single-vector, AARF's ten factors reward breadth of amplification, not just raw impact. cvss_base alone carries the severity of the underlying impact. Don't inflate AARF factors to force a record into a more severe-sounding band; if the honestly computed score feels low relative to the mechanism's intuitive severity, say so in the record's own aivss.notes field rather than adjusting the inputs to hit a target. See references/aivss-scoring.md for the full formula and worked examples of this exact situation.

## confidence_baseline guide

High-signal AVE (hardcoded AWS key, explicit external fetch): 0.85-0.95 Medium-signal (suspicious instruction phrasing): 0.55-0.75 Low-signal (vague, needs corroboration): 0.40-0.55 The scanner adjusts from this baseline via the FP pipeline.

## Reference files

- references/aivss-scoring.md: the AARF formula, how aars is computed from the ten factors, and worked examples spanning MEDIUM through HIGH, including the specific trap of inflating factors to chase a severity band. - references/schema-fields.md: the provenance_vector.entry_class enum (confirmed live against the corpus) and escalation values, distinct from grill-with-docs Q7's detection_layer, a coarser, separate field; don't conflate the two when writing provenance_vector.

Intended uses

• Use Add Ave Record when this documented workflow matches the task.